Model code and data policy

GMD code and data policy

The GMD code and data policy is fully compliant with the Copernicus data policy. Here we explain in particular the requirements in the context of GMD's focus on code and data directly related to numerical model development.

In this document, code refers to computer instructions and algorithms made available as plain text. Here, data refers to any other information that is found outside of the main body of the manuscript and is required to either fully appreciate or reproduce the results presented in the manuscript.

All papers must include a section at the end of the paper before the acknowledgements (please see the manuscript composition for the correct sequence) entitled "Code and/or data availability":

Preferably, this section should contain the instructions for obtaining the model code and/or data, either from the supplement or from an archive with a digital object identifier (DOI). Suitable repositories can be found at the Registry of Research Data Repositories, e.g. ZENODO for model code. After the paper is accepted, a link to the GMD paper should be added to the metadata of the archive.

Although not recommended, and authors will typically be requested to improve on this, it is also possible to provide the code and/or data only upon request via a given point of contact.

If the authors cannot or do not wish to make the code and/or data public (e.g. copyright or licensing restrictions), the reasons must be clearly stated. Note that, for the purpose of the review, the code and/or data must still be made available to the editor. Access must also be granted to the reviewers whilst preserving their anonymity, if this is legally possible.

Although the source code and user manual will not be reviewed formally, the reviewers are free to make general comments on any code or data, if they so wish. During the review process, the ease of model download, compilation, and running of test cases may be assessed.

Copernicus data policy

The output of research is not only journal articles but also data sets, model code, samples, etc. Only the entire network of interconnected information can guarantee integrity, transparency, reuse, and reproducibility of scientific findings. Moreover, all of these resources provide great additional value in their own right. Hence, it is particularly important that data and other information underpinning the research findings are "findable, accessible, interoperable, and reusable" (FAIR) not only for humans but also for machines.

Therefore, Copernicus Publications requests depositing data that correspond to journal articles in reliable (public) data repositories, assigning digital object identifiers, and properly citing data sets as individual contributions. Please find your appropriate data repository in the registry for research data repositories: re3data.org. A data citation in a publication resembles a bibliographic citation and needs to be included in the publication's reference list. To foster the accessibility as well as the proper citation of data, Copernicus Publications requires all authors to provide a statement on the availability of underlying data as the last paragraph of each article (see section data availability). In addition, data sets, model code, video supplements, video abstracts, International Geo Sample Numbers, and other digital assets should be linked to the article through DOIs in the assets tab. With Earth System Science Data (ESSD) Copernicus Publications provides a journal dedicated to the publication of data papers, including peer review of data sets. Authors should consider submitting a data paper to ESSD in addition to their research paper in another journal published by Copernicus Publications.

Preamble

Sound, reproducible scholarship rests upon a foundation of robust, accessible data. For this to be so in practice as well as theory, data must be accorded due importance in the practice of scholarship and in the enduring scholarly record. In other words, data should be considered legitimate, citable products of research. Data citation, like the citation of other evidence and sources, is good research practice and is part of the scholarly ecosystem supporting data reuse.

In support of this assertion, and to encourage good practice, we offer a set of guiding principles for data within scholarly literature, another dataset, or any other research object.

Principles

The Data Citation Principles cover purpose, function and attributes of citations. These principles recognize the dual necessity of creating citation practices that are both human understandable and machine-actionable.

These citation principles are not comprehensive recommendations for data stewardship. And, as practices vary across communities and technologies will evolve over time, we do not include recommendations for specific implementations, but encourage communities to develop practices and tools that embody these principles.

The principles are grouped so as to facilitate understanding, rather than according to any perceived criteria of importance.

1. Importance

Data should be considered legitimate, citable products of research. Data citations should be accorded the same importance in the scholarly record as citations of other research objects, such as publications.

2. Credit and attribution

Data citations should facilitate giving scholarly credit and normative and legal attribution to all contributors to the data, recognizing that a single style or mechanism of attribution may not be applicable to all data.

3. Evidence

In scholarly literature, whenever and wherever a claim relies upon data, the corresponding data should be cited.

4. Unique identification

A data citation should include a persistent method for identification that is machine actionable, globally unique, and widely used by a community.

5. Access

Data citations should facilitate access to the data themselves and to such associated metadata, documentation, code, and other materials, as are necessary for both humans and machines to make informed use of the referenced data.

6. Persistence

Unique identifiers, and metadata describing the data, and its disposition, should persist – even beyond the lifespan of the data they describe.

7. Specificity and verifiability

Data citations should facilitate identification of, access to, and verification of the specific data that support a claim. Citations or citation metadata should include information about provenance and fixity sufficient to facilitate verifying that the specific timeslice, version and/or granular portion of data retrieved subsequently is the same as was originally cited.

8. Interoperability and flexibility

Data citation methods should be sufficiently flexible to accommodate the variant practices among communities, but should not differ so much that they compromise interoperability of data citation practices across communities.